Alternative scenarios for Hungary for the year 2025

The paper presents how the Committee on Futures Research, within Section IX. of the Hungarian Academy of Sciences (HAS), sees the possible futures for Hungary for the year 2025, based on the expertise of Hungarian futurists and social scientists, including the opinions of younger generations. It offers insight to Hungarian society in 18 years from 2007, when the research began. In cooperation with experts coming from diverse scientific backgrounds and with those who feel responsibility for the future and are willing to act upon it, we need to continue discovering our horizon albeit in a different way and to embark on new roads. In summary, we need to change the HOW and the WHAT

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

Bistability through triadic closure

We propose and analyse a class of evolving network models suitable for describing a dynamic topological structure. Applications include telecommunication, on-line social behaviour and information processing in neuroscience. We model the evolving network as a discrete time Markov chain, and study a very general framework where, conditioned on the current state, edges appear or disappear independently at the next timestep. We show how to exploit symmetries in the microscopic, localized rules in order to obtain conjugate classes of random graphs that simplify analysis and calibration of a model. Further, we develop a mean field theory for describing network evolution. For a simple but realistic scenario incorporating the triadic closure effect that has been empirically observed by social scientists (friends of friends tend to become friends), the mean field theory predicts bistable dynamics, and computational results confirm this prediction. We also discuss the calibration issue for a set of real cell phone data, and find support for a stratified model, where individuals are assigned to one of two distinct groups having different within-group and across-group dynamics

Deep Multi-view Learning to Rank

We study the problem of learning to rank from multiple information sources. Though multi-view learning and learning to rank have been studied extensively leading to a wide range of applications, multi-view learning to rank as a synergy of both topics has received little attention. The aim of the paper is to propose a composite ranking method while keeping a close correlation with the individual rankings simultaneously. We present a generic framework for multi-view subspace learning to rank (MvSL2R), and two novel solutions are introduced under the framework. The first solution captures information of feature mappings from within each view as well as across views using autoencoder-like networks. Novel feature embedding methods are formulated in the optimization of multi-view unsupervised and discriminant autoencoders. Moreover, we introduce an end-to-end solution to learning towards both the joint ranking objective and the individual rankings. The proposed solution enhances the joint ranking with minimum view-specific ranking loss, so that it can achieve the maximum global view agreements in a single optimization process. The proposed method is evaluated on three different ranking problems, i.e. university ranking, multi-view lingual text ranking and image data ranking, providing superior results compared to related methods.Comment: Published at IEEE TKD

Evaluation of Urban Improvement on the Islands of the Venice Lagoon: A Spatially-Distributed Hedonic-Hierarchical Approach

This paper presents a model for the evaluation of environmental and urban improvements on the islands of the Venetian lagoon. The model simulates the changes in residential real estate values using a value function integrated in a geographical database which provides spatial distributions of values changes. The fairly weak market signals, fragmented demand and strong externalities, and the scarcity of market data available do not permit the use of econometric models for value appraisal. Appropriate hedonic-hierarchical value functions are calibrated on the basis of a set of indicators of the characteristics of the buildings and the location. Some applications of the model are illustrated simulating two scenarios of future interventions which are actually being discussed or realised and involving the island of Murano, Burano and S. Erasmo in the Venice Lagoon. The interventions considered are: subway beyond the lagoon connecting Murano with Venice and the mainland, and the solution of “high water” problems on Murano, Burano and S. Erasmo.Public work assessment, Property value, Hierarchical analysis

Collaborative learning and co-author students in online higher education: a-REAeduca – collaborative learning and co-authors

The technologies themselves cannot be analyzed as instruments per se, nor can they be exhausted in their relation with science. There is a social and even an individual dimension that affects our own way of relating to society. It is in open education that we have been developing our educational practices. This chapter presents a collaborative learning activity, the curricular unit Materiais e Recursos para eLearning, part of an on-line Master in Pedagogy of eLearning, Universidade Aberta, Portugal. In the present work, the authors dedicate their attention to co-learning and co-research, as processes that help to exemplify some situations, the a-REAeduca. The data collection was supported essentially by the content analysis technique.info:eu-repo/semantics/publishedVersio